Benefits, barriers, and limitations on the use of Hospital Incident Command System
Bibliographic record
Abstract
Hospital Incident Command System (HICS) has been established with the mission of prevention, response, and recovery in hazards. Regarding the key role of hospitals in medical management of events, the present study is aimed at investigating benefits, barriers, and limitations of applying HICS in hospital. Employing a review study, articles related to the aforementioned subject published from 1995 to 2016 were extracted from accredited websites and databases such as PubMed, Google Scholar, Elsevier, and SID by searching keywords such as HICS, benefits, barriers, and limitations. Then, those articles were summarized and reported. Using of HICS can cause creating preparedness in facing disasters, constructive management in strategies of controlling events, and disasters. Therefore, experiences indicate that there are some limitations in the system such as failure to assess the strength and severity of vulnerabilities of hospital, no observation of standards for disaster management in the design, constructing and equipping hospitals, and the absence of a model for evaluating the system. Accordingly, the conducted studies were investigated for probing the performance HICS. With regard to the role of health in disaster management, it requires advanced international methods in facing disasters. Using accurate models for assessing, the investigation of preparedness of hospitals in precrisis conditions based on components such as command, communications, security, safety, development of action plans, changes in staff's attitudes through effective operational training and exercises and creation of required maneuvers seems necessary.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".